code: def process_item(args, filename):
      path = osp.join(args.mdetr_anno_dir, filename)
      coco = COCO(path)
  
      ids = list(sorted(coco.imgs.keys()))
  
      out_results = []
      for img_id in tqdm(ids):
          if isinstance(img_id, str):
              ann_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=0)
          else:
              ann_ids = coco.getAnnIds(imgIds=img_id, iscrowd=0)
          annos = coco.loadAnns(ann_ids)
          if not has_valid_annotation(annos):
              continue
  
          img_info = coco.loadImgs(img_id)[0]
          file_name = img_info["file_name"]
          caption = img_info["caption"]
  
          regions = {}
  
          for anno in annos:
              box = anno["bbox"]
              tokens_positive = anno["tokens_positive"]
              x1, y1, w, h = box
              inter_w = max(
                  0, min(x1 + w, int(img_info["width"])) - max(x1, 0)
              )
              inter_h = max(
                  0, min(y1 + h, int(img_info["height"])) - max(y1, 0)
              )
              if inter_w * inter_h == 0:
                  continue
              if anno["area"] <= 0 or w < 1 or h < 1:
                  continue
  
              if anno.get("iscrowd", False):
                  continue
              bbox_xyxy = [
                  x1,
                  y1,
                  min(x1 + w, int(img_info["width"])),
                  min(y1 + h, int(img_info["height"])),
              ]
  
              tokens_positive = sorted(
                  tokens_positive, key=lambda x: x[0]
              )
  
              phrase = []
              pre_end_index = -10
              for token in tokens_positive:
                  start_index = token[0]
                  end_index = token[1]
                  if pre_end_index + 1 == start_index:
                      if caption[token[0] - 1] == " ":
                          phrase[-1] = (
                              phrase[-1]
                              + " "
                              + caption[token[0] : token[1]]
                          )
                      else:
                          phrase.append(caption[token[0] : token[1]])
                  else:
                      phrase.append(caption[token[0] : token[1]])
                  pre_end_index = end_index
  
              key = " ".join(phrase)
  
              if key not in regions:
                  regions[key] = {
                      "bbox": bbox_xyxy,
                      "phrase": phrase,
                      "tokens_positive": tokens_positive,
                  }
              else:
                  old_box = regions[key]["bbox"]
                  if isinstance(old_box[0], list):
                      old_box.append(bbox_xyxy)
                  else:
                      old_box = [old_box, bbox_xyxy]
  
                  regions[key]["bbox"] = old_box
  
          out_dict = {
              "filename": file_name,
              "height": int(img_info["height"]),
              "width": int(img_info["width"]),
              "grounding": {"caption": caption},
          }
  
          region_list = []
          for key, value in regions.items():
              phrase = value["phrase"]
              if len(phrase) == 1:
                  phrase = phrase[0]
              region_list.append(
                  {
                      "bbox": value["bbox"],
                      "phrase": phrase,
                      "tokens_positive": value["tokens_positive"],
                  }
              )
          out_dict["grounding"]["regions"] = region_list
          out_results.append(out_dict)
  
      if args.out_dir is None:
          out_path = osp.join(
              args.mdetr_anno_dir, filename[:-5] + "_vg.json"
          )
      else:
          out_path = osp.join(args.out_dir, filename[:-5] + "_vg.json")
  
      with jsonlines.open(out_path, mode="w") as writer:
          writer.write_all(out_results)
      print(f"save to {out_path}")
  
file_path: mmdetection/tools/dataset_converters/refcoco2odvg.py
imports: import argparse
  import os.path
  import jsonlines
  from pycocotools.coco import COCO
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: process_item(args, filename) without return types